Autoregressive Method Research Articles

Fiscal effects of exchange rate devaluation and capital flows to emerging countries

In the assessment of governments’ fiscal performance, exchange rates play some roles while capital movements could serve as determinant of fiscal discipline. This study examined the effects of exchange rate devaluation, and capital inflows, on budgetary spending, and the interactions among the variables using the Bayesian vector autoregression (BVAR) and sys-generalized method of moments (GMM) estimators with 1,184 panel observations. The study covers 37 emerging nations. The variables had a co-integrating connection, demonstrating a long-run link between the variables studied. Having executed the Gibbs sampling for simulation efficiently, our Markov Chain Monte Carlo (MCMC) simulation converged appropriately. The sampling efficiency parameter is equal to 0.96257, close to 1. The Monte Carlo standard errors (MCSE) are extremely low at 0.000 with an implication of adequate precision in the BVAR model estimation. The results disclose that a 1 percent devaluation shock compressed fiscal spending by 0.56 percent and a shock to capital inflows stimulated 0.99% growth in fiscal spending. The 95 percent credible interval suggests a considerable size of effects on devaluation and capital flows. Accordingly, managing the exchange rate can be a valuable tool for managing capital shortage in Africa. Rather than increase government spending, governments should concentrate on revenue generation by utilizing an effective exchange rate policy to influence the national pattern of product diversification.

Transient Interference Suppression Method Based on an Improved TK Energy Operator and Fuzzy Reasoning

Since sky wave over-the-horizon radar is seriously affected by transient interference, a transient interference suppression method based on an improved Teager-Kaiser energy operator and fuzzy reasoning is proposed. Initially, due to the extraction of the information entropy of slow time samples, the resolution ability of the energy operator to short-term fluctuation characteristics is improved and used to more accurately locate transient interference. Then, four evaluation indexes are constructed to quantify the confidence of the data reconstruction results obtained via the autoregressive, subspace and smoothing methods. Finally, Dempster and Shafer evidence theory is introduced to conduct fuzzy reasoning on the various reconstruction results, and comprehensive results are obtained. The experimental results show that our proposed method has evident advantages in locating weak interference, preventing clutter broadening and maintaining high-speed target energy.

Effect of macroeconomic indicators on stock price indices with the vector error correction model approach

The capital market as one of the important instruments in the economy requires indicators to determine growth in it. The Composite Stock Price Index (IHSG) is used as one of the capital market indicators with various influencing factors. This research was conducted to test and analyze the effect of inflation, exchange rate, the Bank Indonesia (BI) rate, and money supply (M2) on the Jakarta Composite Index (JCI) in the period from January 2017 to March 2022. In this study, quantitative descriptive research was carried out with JCI as the dependent variable and inflation, exchange rate, BI-rate, and money supply as independent variables. The research used the vector autoregression (VAR) method using the EViews 12 analysis tool. From the tests conducted it showed that between variables only had a one-way relationship, with the R squared value indicating that the independent variables in the model were able to explain changes in the dependent variable that occurred. The variables in the model are also indicated to have a simultaneous effect on the dependent variable; this is based on a high F-statistic value.

Forecasting of electricity consumption by industrial enterprises with a continuous nature of production

The development of models for forecasting electricity consumption is a complex process, due to the non-linear dependence of electricity consumption on factors that affect the forecast indicators. Since the current forecasting methods do not take into account this non-linearity, the difference between the actual and forecast indicators of electricity consumption often exceeds the allowable values. To determine the required forecast indicators with high accuracy is the use of artificial intelligence methods. In this paper, when predicting electricity consumption, the method of autoregression of the integrated moving average is used. An enlarged block diagram of the algorithm for predicting power consumption using the ARIMA method has been developed.

PNG-Stega: Progressive Non-Autoregressive Generative Linguistic Steganography

The autoregressive-based model with the left-to-right generation order has been a predominant paradigm for generative linguistic steganography. However, such steganography does not perform well on semantic control and content planning, which is forced by the secret message during the generation process. To mitigate this issue and efficiently produce high-quality steganographic texts (stegotexts), we present a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> rogressive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> on-autoregressive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</i> enerative linguistic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stega</i> nography (PNG-Stega), which encodes secret messages and extends the context to generate stegotexts in a multi-round insertion manner. Each round continuously refines the generated steganographic sequences on the premise of the global information of the previous round, while striving to decline the adverse effects of steganographic encoding on text quality. Moreover, for enhancing the semantic internal dependency of stegotexts, we utilize a constraint word sequences extraction scheme to obtain keywords to initialize the skeleton of targeted stegotexts, then expand the existing keywords with insertion operations. Experimental results demonstrate that PNG-Stega outperforms compared methods in terms of imperceptibility and anti-steganalysis ability. In particular, PNG-Stega provides high information hiding efficiency, even exceeding the autoregressive methods by around 2 times.

Effects of Yuan to Naira and Dollar to Naira Exchange Rates on Economic Growth in Nigeria

This study aims to explore the effect of the Yuan-Naira and Dollar-Naira exchange rates on economic growth in Nigeria. The study covers the period 2010Q1–2021Q4 quarterly data. Using the Vector Autoregressive method, the study finds the following: (1) inn

Forecasting poverty in East Java using vector autoregressive method and vector error correction model

People experiencing poverty are people who are unable to fulfil their basic needs. A region with a dense population is prone to problems overcoming poverty. In this instance, the gross regional domestic product, the human development index, and the open unemployment rate are the variables impacting poverty. Therefore, more study is required to address this issue of poverty. The vector autoregressive and error correction models are two possible approaches. The East Java Central Bureau of Statistics provided the data, which included gross regional domestic product, human development index, open unemployment rate, and percentage of poverty. Forecasting the number of poverty people is obtained using estimates from data that can affect forecasting results. In this article, the best forecasting results are obtained with an RMSE value of 21.51062 using the vector error correction model, namely with a percentage of poverty value of 7.2619.

Carbon-dioxide emissions management in Sub-Saharan Africa – the irrelevance of natural resource rent as a corrective policy tool

ABSTRACT This paper revisits the role of natural resource rent in explaining and regulating CO2 emissions in Sub-Saharan Africa (SSA). Three variants of CO2 emissions are considered: territorial CO2 emissions, consumption-based CO2 emissions, and CO2 emission intensity. Panel-corrected standard error and panel autoregressive distributed lag estimation methods were applied. Results show that natural resource rent has a positive effect on consumption-based CO2 emissions, and a negative effect on CO2 emission intensity, but has no effect on territorial CO2 emissions. The results show that while high resource rent in SSA appears to finance consumption of pollution-laden imported goods, it worsens neither territorial CO2 emissions nor CO2 emission intensity. Given that importation of dirty goods is an economic system failure which is not imputable to resource rent, it is safe to conclude that, resource rent does not contribute to rising CO2 emissions in SSA.

Modeling of occupant behavior considering spatial variation: Geostatistical analysis and application based on American time use survey data

Numerous occupant behavior (OB) models that simulate occupancy, activity and action at home have been developed to improve the accuracy and quality of energy demand estimations. Previous studies have revealed that the consideration of inter-occupant diversity improves the performance of OB models. However, existing models ignore spatial variation in OBs or partially consider it using a simple method without evaluating whether it is sufficient. Moreover, the modeling method to reproduce the spatial variation is missing. This study aims to develop a modeling method that can effectively reproduce spatial variation in OBs using American time use data. The global Moran’s index test confirmed that spatial variations exist in OBs; however, they differ by time of day and activities for studied population. Subsequently, two spatial variation representations were generated using the ordinary kriging and spatial autoregressive methods. Finally, three spatial logistic regression models that consider spatial variations were developed and evaluated. The developed models generated smaller errors and higher inter-occupant diversity than the conventional logistic regression models at the state level. The established method is applicable to any country and region. Using higher spatial resolution and richer time use datasets may further improve OB models to model region-specific characteristics of building energy demand.

The Unreliability of Output-Gap Estimates in Real Time

Most research on monetary policy assumes availability of information regarding the current state of economy, at the time of the policy decision. A key challenge for policy-makers is to find indicators that give a clear and precise signal of the state of the economy in real time—that is, when policy decisions are actually taken. One of the indicators used to asses the economic condition is the output gap; and the estimates of output gap from real time data misrepresents the true state of economy. So the policy decisions taken on the basis of real time noisy data are proved wrong when true data become available. Within this context we find evidence of wrong estimates of output gap in real time data. This is done by comparing estimates of output gap based on real time data with that in the revised data. The quasi real time data are also constructed such that the difference between estimates of output gap from real time data and that from quasi real time data reflects data revision and the difference between estimates of output gap from final data and that from quasi real time data portray other revisions including end sample bias. Moreover, output gap is estimated with the help of five methods namely the linear trend method, quadratic trend method, Hordrick-Prescott (HP) filter, production function method, and structural vector autoregressive method. Results indicate that the estimates of output gap in real time data are different from what have been found in final data but other revisions, compared to data revisions, are found more significant. Moreover, the output gap measured using all the methods, except the linear trend method, appropriately portray the state of economy in the historical context. It is also found that recessions can be better predicted by real time data instead of revised data, and final data show more intensity of recession compared with what has been shown in real time data. JEL Classification: E320 Keywords: Data Uncertainty, Measurement Uncertainty, Output Gap, Business Cycle, Economic Activity

Investigating the Relationship between Misery Index and Patients' Out-of-pocket Payments in Iran's Health System

Background: One of the criteria for measuring the performance of governments and their survival is the degree of success in reducing and stabilizing the misery index. The misery index is obtained from the sum of inflation and unemployment. Inflation and unemployment are the most important factors affecting family's income. If the misery index is high, families will have less money to buy their consumer goods, including the use of healthcare services and treatment. Therefore, this research has evaluated the relationship between misery index and patients' out-of-pocket payments in Iran's health system.&#x0D; Methods: Using time series data of 1998-2019, this study has investigated the relationship between misery index and patients' out-of-pocket payments in Iran's health system. For this purpose, the Dickey-Fuller test was used to check the durability of the variables, the Todayamamoto causality test was used to check the causality relationship between the variables, and the autoregression method with distribution breaks was used to check the existence of a long-term relationship. In addition, the data and results were analyzed using Eview 9 software.&#x0D; Results: The results showed that there was a bi-directional causal relationship between the misery index and the out-of-pocket payments of patients in the health system. Furthermore, with increasing 1 unit of misery index 1.27 units of out-of-pocket payments increased. The correction error coefficient was 0.312, which meant this amount was adjusted each period by 0.312 from short-term imbalance to achieve long-term balance. In other words, it takes more than 3 years and less than 4 years to eliminate the short-term imbalances and reach its long-term relationship.&#x0D; Conclusion: As the misery index increases, patients' out-of-pocket payments also increase. Therefore, the government should reduce the out-of-pocket payments of patients in the health system by adopting appropriate policies to reduce unemployment and inflation. Basic insurance coverage for essential services for patients should be increased. The extent of complementary medical insurance should be increased at the community level, especially in the lower deciles. In fact, the government should bear the cost of this part of the insurance for the four lower deciles. This is done in order to reduce the patients' out-of-pocket payments.

The impact of unpredictable resource prices and equity volatility in advanced and emerging economies: An econometric and machine learning approach

Global stock markets are incredibly unpredictable. Resource prices have a significant market impact on varying securities. With the use of cutting-edge technology like artificial intelligence, analysts and researchers are employing various machine learning techniques and econometrics methodologies to anticipate stock price trends in order to better comprehend stock market volatility. Volatility is the degree of variation in a time sequence of market rates. Stock market equity returns depend on the business output where the investor has trust in high and low equity. This research explores the interaction between industrialized and developing economies' market volatility relationships between 2000 and 2020 as well as the aforementioned impacts taking place on developing financial prudence worldwide. The aim of the study is to integrate an appropriate GARCH framework to estimate the uncertainty dependent on market conditions in the European Union, the Pacific, South America, Latin America, East Asia, West Asia and South Asia stock return indices. The Generalized Auto-Regressive Conditional Heteroscedasticity method is used for analyzing the effect of updates from the USA that influences the returns of S&P 500 globally as well as European Union, Pacific, South American, Latin American, East Asian, West Asian and South Asian indices returns. For capital markets of the world, there is a significant gap in equity return uncertainty. Such results have major effects on investors looking to diversify their portfolios. For international and domestic institutional shareholders, this paper is significant. The impact of international institutional investors' investments and effects of the growth of the equity market return may be omitted as the analysis is restricted exclusively to the European Union, the Pacific, South America, Latin America, East Asia, West Asia, and South Asia.

Transformer Implementation for Short Term Electricity Load Forecasting, Case Study: Bali, Indonesia

Short term load forecasting is a crucial process in ensuring optimal and reliable operation of electric power system which is critical in sustaining highly technological economies. Various approaches and methods have been implemented in forecasting electricity load of a system including statistical methods such as auto regression and machine learning methods such as support vector machine and also deep learning methodology as recurrent neural network methodology which gain popularity in electricity load forecasting nowadays. In this paper, Transformer as a deep learning methodology is used to forecast hourly electricity load in Bali Area. Three lookback days scenario and ten days of forecast period are used to evaluate the performance of the Transformer models. This study suggest that although higher lookback days will give more complicated model due to increasing number of parameters involved, the best overall prediction performance are given by transformer model with 1 day of lookback period. The three model in this study also tend to have low prediction performance in predicting electricity load for weekend or holiday period. Future study using multivariate transformer model is suggested to improve the prediction performance of the transformer model in predicting electricity load in Bali area.

A Pyramid Semi-Autoregressive Transformer with Rich Semantics for Sign Language Production

As a typical sequence to sequence task, sign language production (SLP) aims to automatically translate spoken language sentences into the corresponding sign language sequences. The existing SLP methods can be classified into two categories: autoregressive and non-autoregressive SLP. The autoregressive methods suffer from high latency and error accumulation caused by the long-term dependence between current output and the previous poses. And non-autoregressive methods suffer from repetition and omission during the parallel decoding process. To remedy these issues in SLP, we propose a novel method named Pyramid Semi-Autoregressive Transformer with Rich Semantics (PSAT-RS) in this paper. In PSAT-RS, we first introduce a pyramid Semi-Autoregressive mechanism with dividing target sequence into groups in a coarse-to-fine manner, which globally keeps the autoregressive property while locally generating target frames. Meanwhile, the relaxed masked attention mechanism is adopted to make the decoder not only capture the pose sequences in the previous groups, but also pay attention to the current group. Finally, considering the importance of spatial-temporal information, we also design a Rich Semantics embedding (RS) module to encode the sequential information both on time dimension and spatial displacement into the same high-dimensional space. This significantly improves the coordination of joints motion, making the generated sign language videos more natural. Results of our experiments conducted on RWTH-PHOENIX-Weather-2014T and CSL datasets show that the proposed PSAT-RS is competitive to the state-of-the-art autoregressive and non-autoregressive SLP models, achieving a better trade-off between speed and accuracy.

MTKM di Indonesia Efektivitas Mekanisme Transmisi Kebijakan Moneter di Indonesia Melalui Jalur Kredit

The aim of this study is to see how effective the monetary policy transmission mechanism is through the interest rate channel in Indonesia. This research was conducted to see how effective the monetary policy transmission mechanism is through the interest rate channel using data from 197 – 2017. This research uses the Vector Autoregression (VAR) method in its testing process. The results of this study indicate that the credit channel is an effective channel for achieving the ultimate goal of monetary policy in Indonesia.

The Economic Value of Water Ecology in Sponge City Construction Based on a Ternary Interactive System.

Ecological water resources occupy a vital position in the national economy; without sufficient ecological water resources, the construction and economic development of sponge cities would be seriously restricted. Appropriately, the Chinese government proposed that sponge city planning should be carried out in accordance with the number of available ecological water resources. The government therefore put forward the method of conservation and intensification to solve the problem of water shortage. This paper highlights the interactions between ecological water resources, sponge cities, and economic development in northern China, starting with the interaction and mechanism of action that concerns ecological water resource utilization, sponge cities, and economic development. In the empirical test, the dynamic changes of the three indicators were analyzed empirically using the panel data vector autoregression method, and the dynamic relationship of each factor was measured using generalized moment estimation. It was found that ecological water resources are a key factor in promoting regional economic development, and the relationship between ecological water resources and sponge cities is both supportive and constraining; therefore, the constraints that ecological water resources place on sponge cities also indirectly affects economic development. To disconnect the use of water and ecological resources from economic development, it is necessary to note the following: the feedback effect of economic development and the resolution of the contradiction between sponge cities, water, and ecological resource use.

Counterfactual analysis of the impact of the first two waves of the COVID-19 pandemic on the reporting and registration of missing people in India

The primary duty of law enforcement agencies is to ensure that a victim has the necessary information and access to the relevant tools required to seek justice. In India, complex cases such as bodily offences and property crimes capture the work and efforts of many agencies involved; however, cases related to missing persons are not often accorded similar priority or seriousness. The COVID-19 pandemic and subsequent lockdowns have added further challenges to this scenario. The government-mandated lockdowns in Tamil Nadu generally exacerbated difficult socio-economic and living conditions, thereby directly or indirectly contributing to an increased load of missing person cases. This study aims to assess and identify the impact of mobility on reporting and registration of missing persons. By adopting an auto-regressive neural networks method, this study uses a counterfactual analysis of registered missing person cases during the government-mandated lockdowns in response to the global pandemic in 2020 and 2021. The registered cases are calculated based on the daily count of cases for eleven years in Tamil Nadu, India. The lockdowns identify eight different time windows to determine the impact of mobility on the registration of cases. While there has been no significant or drastic change over the pre-pandemic period, during the pandemic, especially during the restrictive phases of the pandemic, there was a sharp fall in cases compared to the counterfactual predicted (effect sizes: −0.981 and −0.74 in 2020 and 2021), signalling towards a choked mechanism of reporting. In contrast, when most mobility restrictions were removed, an increase in cases (effect sizes of +0.931 and 0.834 in 2020 and 2021) pointed to restored and enabled reporting channels. The research findings emphasise the significance of mobility as a factor in influencing the reporting and registration of missing persons and the need to ensure this continues to help families find redress.

Investigating the Index of Sustainable Development and Reduction in Greenhouse Gases of Renewable Energies

Considering the limited resources of fossil energy and the problems caused by the emission of greenhouse gases, it is necessary to pay more attention to renewable energies, because in this way, the goals of sustainable development can be achieved. The importance of renewable energies in sustainable development, reducing greenhouse gases and increasing energy security on the one hand, and the need for financial resources and large investments for renewable energy projects on the other hand, doubles the role and importance of financial development in the development of renewable energies. Considering the importance of this issue, the present study examines the impact of the development of modern facilities and renewable energy technology. In this study, dynamic interactions in the Sustainable-Energy-Energy Development Pattern of carbon dioxide are investigated using the Bayesian Vector Auto Regression (BVAR) method. One of the most important indicators for evaluating sustainable development is the modified pure arrangement (GS). For this purpose, this index was used as a sustainable development index. The results indicate that the effect of positive impulse on renewable and renewable energy consumption on sustainable development in Uganda is positive. In addition, the positive shock of renewable and renewable energy consumption increases the emissions of carbon dioxide pollutants to a different extent. In addition, the effect of the growth of sustainable development index on renewable energy consumption and renewal energy consumption is (CO2) negative. The research results show that based on the RMSE criterion, the former SSVS-Full function was used to investigate the impact of renewable energy consumption on sustainable development and the independent Normal-Wish art function was used. Therefore, in this research, the dynamic relationships between sustainable development, energy consumption (separately from renewable and non-renewable energy) and CO2 emissions are investigated.

Modeling for the Relationship between Monetary Policy and GDP in the USA Using Statistical Methods

The Federal Reserve has played an arguably important role in financial crises in the United States since its creation in 1913 through monetary policy tools. Thus, this paper aims to analyze the impact of monetary policy on the United States’ economic growth in the short and long run, measured by Gross Domestic Product (GDP). The Vector Autoregressive (VAR) method explores the relationship among the variables, and the Granger causality test assesses the predictability of the variables. Moreover, the Impulse Response Function (IRF) examines the behavior of one variable after a change in another, utilizing the time-series dataset from the first quarter of 1959 to the second quarter of 2022. This work demonstrates that expansionary monetary policy does have a positive impact on economic growth in the short term though it does not last long. However, in the long term, inflation, measured by the Consumer Price Index (CPI), is affected by expansionary monetary policy. Therefore, if the Federal Reserve wants to cease the expansionary monetary policy in the short run, this should be done appropriately, with the fiscal surplus, to preserve its credibility and trust in the US dollar as a global store of value asset. Also, the paper’s findings suggest that continuous expansion of the Money Supply will lead to a long-term inflationary problem. The purpose of this research is to bring the spotlight to the side effects of expansionary monetary policy on the US economy, but also allow other researchers to test this model in different economies with different dynamics.

Estimating Inclusiveness of Growth in Sub-Sahara African Countries: a VAR Approach

The study examined the interaction among income inequality, economic growth, and poverty with a view of assessing the extent of inclusiveness of growth in selected African countries. Using a dataset from World Development Indicators (WDI) and Standardized World Income Inequality Dataset (SWIID), the study adopted Vector AutoRegression (VAR) method in its econometric analysis. Its findings show that only "West" African countries exhibit partial traits of inclusiveness of growth. Thus, the study recommends the provision and implementation of distributional and growth-oriented macroeconomic policies, as well as investment in public infrastructure to spread the benefits of growth to all in African countries.